Predictive mobility in cellular networks

ABSTRACT

Methods, systems, and devices are described for managing wireless communications. In the methods, systems, and devices, a subset of a set of neighboring cells is identified for measurement by a mobile device. The subset of neighboring cells is identified based on historical information associated with mobility patterns of the mobile device.

CROSS-REFERENCE

The present application claims priority to U.S. Provisional Patent Application No. 61/860,789, filed Jul. 31, 2013, entitled “PREDICTIVE MOBILITY IN CELLULAR NETWORKS,” the entire disclosure of which is incorporated herein by reference for all purposes.

BACKGROUND

The present description relates generally to wireless communication, and more specifically to adapting the behavior of mobile devices based on observed mobility trends. Wireless communications systems are widely deployed to provide various types of communication content such as voice, video, packet data, messaging, broadcast, and so on. These systems may be multiple-access systems capable of supporting communication with multiple users by sharing the available system resources (e.g., time, frequency, space and power). Examples of such multiple-access systems include code-division multiple access (CDMA) systems, time-division multiple access (TDMA) systems, frequency-division multiple access (FDMA) systems, and orthogonal frequency-division multiple access (OFDMA) systems.

Generally, a wireless multiple-access communications system may include a number of base stations, each simultaneously supporting communication for multiple mobile devices. Base stations may communicate with mobile devices on downstream and upstream links. Each base station has a coverage range, which may be referred to as the coverage area of the cell.

When a mobile device connected to a base station of a first cell moves out of the coverage area of the first cell, the first cell typically requests signal strength measurements for all neighboring cells of the mobile device for use in identifying a handover target candidate. These signal strength measurements and reports may consume power, thereby reducing the battery life of the mobile device. Additionally, these signal strength measurements and reports may introduce delays into the handover process, thereby increasing the likelihood that a call or connection is lost during the handover. In addition, significant signaling resources are used in communicating the measurement requests and reports between the base stations and the mobile device before and during the handover process.

While a mobile device in idle mode (i.e. a mode where the device is camping on the cell and not actively communicating information with the base station) does not communicate measurements to the network, the mobile device may still monitor signal strength from neighboring cells so as to identify good candidates for reselection. This monitoring may consume a significant percentage of the mobile device's power consumption.

SUMMARY

The described features generally relate to one or more improved systems, methods, and/or apparatuses for predictive mobility in cellular networks. Further scope of the applicability of the described methods and apparatuses will become apparent from the following detailed description, claims, and drawings. The detailed description and specific examples are given by way of illustration only, since various changes and modifications within the spirit and scope of the description will become apparent to those skilled in the art.

According to a first set of illustrative embodiments, a method for managing wireless communications, may include identifying a subset of a set of neighboring cells for measurement by a mobile device, the identification based on historical information associated with mobility patterns of the mobile device.

In certain examples, the method may be performed by a network entity, the mobile device, or a combination of the two.

In certain examples, the historical information may be received from a server, and the historical information may include a serving cell history of the mobile device over a predetermined period of time collected by the server. The historical information may further include a history of neighboring cells for the mobile device.

In certain examples, the mobile device may collect and store a serving cell history of the mobile device over a predetermined period of time, and the historical information may include the serving cell history. The mobile device may further collect and store a history of neighboring cells for the mobile device, and the historical information may further include the history of neighboring cells.

In certain examples, the subset may include a single neighboring cell. A signal strength associated with the single neighboring cell may be measured, and a handover or cell reselection of the mobile device to the single neighboring cell may be performed when the signal strength is greater than a threshold level. The mobile device may determine not to perform measurements of neighboring cells other than the single neighboring cell when the signal strength associated with the single neighboring cell is greater than the threshold level.

In certain examples, identifying the subset may include determining a quality metric for each of the neighboring cells in the set, the subset including the neighboring cells that have a quality metric greater than a threshold level. The quality metric of each neighboring cell is based on at least a data rate associated with the neighboring cell, or an ability of the neighboring cell to perform offloading to an alternate radio access technology, or a projected amount of time for which the mobile device will remain connected to the neighboring cell. The neighboring cells in the subset may be ranked according to their respective quality metrics.

In certain examples, a confidence level may be determined for each of the neighboring cells in the set. The quality metric of each cell may be based on at least the confidence level of that cell, and the subset may include the neighboring cells that have a confidence level greater than a threshold level. In certain examples, one of the neighboring cells in the subset with a confidence level greater than a threshold level may be identified, and a blind handover of the mobile device to the one neighboring cell may be performed.

In certain examples, a frequency with which measurements are performed by the mobile device for the neighboring cells may be determined based on the historical information.

In certain examples, at least one of the neighboring cells may be excluded from the subset based at least on a current speed of the mobile device and a signal strength of the at least one of the neighboring cells. The at least one of the neighboring cells may be excluded based on a determination that performing a handover to the at least one of the neighboring cells will result in a ping pong effect.

In certain examples, at least one cell measurement associated with handover or reselection may be adjusted based on the historical information.

In certain examples, at least one handover or reselection criterion parameter may be adjusted based on the historical information.

In certain examples, an air interface of the mobile device may be selectively enabled or disabled based on the historical information and a current location of the mobile device.

In certain examples, the mobility patterns of the mobile device may include a route and schedule between a first location and a second location. Additionally or alternatively, the mobility patterns of the mobile device may include a location and a period of time during which the mobile device remains at the location.

According to a second set of illustrative embodiments, an apparatus for managing wireless communications, includes a processor and a memory in electronic communication with the processor. The memory may embody instructions, the instructions being executable by the processor to identify a subset of a set of neighboring cells for measurement by a mobile device, the identification based on historical information associated with mobility patterns of the mobile device.

In certain examples, the instructions may be executable by the processor to perform one or more aspects of the functionality described above with respect to the first set of illustrative embodiments.

According to a third set of illustrative embodiments, an apparatus for managing wireless communications includes means for identifying a subset of a set of neighboring cells for measurement by a mobile device, the identification based on historical information associated with mobility patterns of the mobile device.

In certain examples, the apparatus may further include means for performing one or more aspects of the functionality described above with respect to the first set of illustrative embodiments.

According to a fourth set of illustrative embodiments, a computer program product for managing wireless communications includes a non-transitory computer-readable storage medium having instructions executable by a processor to identify a subset of a set of neighboring cells for measurement by a mobile device, the identification based on historical information associated with mobility patterns of the mobile device.

In certain examples, the instructions may be further executable by the processor to perform one or more aspects of the functionality described above with respect to the first set of illustrative embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of the present invention may be realized by reference to the following drawings. In the appended figures, similar components or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

FIG. 1 shows a block diagram of a wireless communications system, according to one aspect of the principles described herein;

FIG. 2 shows a diagram of an example of device mobility in a wireless communications system, according to one aspect of the principles described herein;

FIGS. 3A-3C show a diagrams of other examples of device mobility in a wireless communications system, according to one aspect of the principles described herein;

FIG. 4 shows a diagram of an example of communications between devices associated with a handover in a wireless communications system, according to one aspect of the principles described herein;

FIG. 5 shows a diagram of an example of communications between devices associated with a handover in a wireless communications system, according to one aspect of the principles described herein;

FIG. 6 shows a diagram of an example of communications between devices associated with a handover in a wireless communications system, according to one aspect of the principles described herein;

FIG. 7 shows a block diagram of a wireless communications system, according to one aspect of the principles described herein;

FIG. 8 shows a block diagram of a wireless communications system, according to one aspect of the principles described herein;

FIG. 9 shows a block diagram of one example of a mobile device, according to one aspect of the principles described herein;

FIG. 10 shows a block diagram of one example of a base station, according to one aspect of the principles described herein;

FIG. 11 shows a flowchart diagram of a method for managing wireless communications, according to one aspect of the principles described herein;

FIG. 12 shows a flowchart diagram of a method for managing wireless communications, according to one aspect of the principles described herein;

FIG. 13 shows a flowchart diagram of a method for managing wireless communications, according to one aspect of the principles described herein;

FIG. 14 shows a flowchart diagram of a method for managing wireless communications, according to one aspect of the principles described herein;

FIG. 15 shows a flowchart diagram of a method for managing wireless communications, according to one aspect of the principles described herein; and

FIG. 16 shows a flowchart diagram of a method for managing wireless communications, according to one aspect of the principles described herein.

DETAILED DESCRIPTION

Methods, systems, and devices are provided that may be used to improve network and/or mobile device performance based on learning and predicting the behavior of a mobile device (e.g., mobile phone, laptop, tablet, etc.) user. For a mobile device user, for example, using predictive behavior may involve identifying a subset of one or more neighboring cells for measurement by the mobile device using historical information associated with mobility patterns of the mobile device. These measurements may then be used to hand off the mobile device to the identified (i.e., predicted) cell as part of a handover or a cell reselection, for example.

Thus, the following description provides examples, and is not limiting of the scope, applicability, or configuration set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the spirit and scope of the disclosure. Various embodiments may omit, substitute, or add various procedures or components as appropriate. For instance, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Also, features described with respect to certain embodiments may be combined in other embodiments.

Techniques described herein may be used for various wireless communications systems such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and other systems. The terms “system” and “network” are often used interchangeably. A CDMA system may implement a radio technology such as CDMA2000, Universal Terrestrial Radio Access (UTRA), etc. CDMA2000 covers IS-2000, IS-95, and IS-856 standards. IS-2000 Releases 0 and A are commonly referred to as CDMA2000 1X, 1X, etc. IS-856 (TIA-856) is commonly referred to as CDMA2000 1xEV-DO, High Rate Packet Data (HRPD), etc. UTRA includes Wideband CDMA (WCDMA) and other variants of CDMA. A TDMA system may implement a radio technology such as Global System for Mobile Communications (GSM). An OFDMA system may implement a radio technology such as Ultra Mobile Broadband (UMB), Evolved UTRA (E-UTRA), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Flash-OFDMA, etc. UTRA and E-UTRA are part of Universal Mobile Telecommunication System (UMTS). 3GPP Long Term Evolution (LTE) and LTE-Advanced (LTE-A) are new releases of UMTS that use E-UTRA. UTRA, E-UTRA, UMTS, LTE, LTE-A, and GSM are described in documents from an organization named “3rd Generation Partnership Project” (3GPP). CDMA2000 and UMB are described in documents from an organization named “3rd Generation Partnership Project 2” (3GPP2). The techniques described herein may be used for the systems and radio technologies mentioned above as well as other systems and radio technologies. The description below, however, describes an LTE system for purposes of example, and LTE terminology is used in much of the description below, although the techniques are applicable beyond LTE applications.

FIG. 1 is a block diagram conceptually illustrating an example of a wireless communications system 100, in accordance with an aspect of the present disclosure. The wireless communications system 100 includes base stations (or cells) 105, mobile devices 115, and a core network 130. The base stations 105 may communicate with the mobile devices 115 under the control of a base station controller (not shown), which may be part of the core network 130 or the base stations 105 in various embodiments. Base stations 105 may communicate control information and/or user data with the core network 130 through backhaul links 132. In certain embodiments, the base stations 105 may communicate, either directly or indirectly, with each other over backhaul links 134, which may be wired or wireless communication links. The wireless communications system 100 may support operation on multiple carriers (waveform signals of different frequencies). Multi-carrier transmitters can transmit modulated signals simultaneously on the multiple carriers. For example, each communication link 125 may be a multi-carrier signal modulated according to the various radio technologies described above. Each modulated signal may be sent on a different carrier and may carry control information (e.g., reference signals, control channels, etc.), overhead information, data, etc.

The base stations 105 may wirelessly communicate with the mobile devices 115 via one or more base station antennas. Each of the base stations 105 sites may provide communication coverage for a respective coverage area 110. In some embodiments, base stations 105 may be referred to as a base transceiver station, a radio base station, an access point, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a NodeB, eNodeB, Home NodeB, a Home eNodeB, or some other suitable terminology. The coverage area 110 for a base station may be divided into sectors making up only a portion of the coverage area (not shown). The wireless communications system 100 may include base stations 105 of different types (e.g., macro, micro, and/or pico base stations). There may be overlapping coverage areas for different technologies.

In certain embodiments, the wireless communications system 100 is an LTE/LTE-A network communication system. In LTE/LTE-A network communication systems, the terms evolved Node B (eNodeB) may be generally used to describe the base stations 105. The wireless communications system 100 may be a Heterogeneous LTE/LTE-A network in which different types of eNodeBs provide coverage for various geographical regions. For example, each eNodeB 105 may provide communication coverage for a macro cell, a pico cell, a femto cell, and/or other types of cell. A macro cell generally covers a relatively large coverage area (e.g., several kilometers in radius) and may allow unrestricted access by UEs 115 with service subscriptions with the network provider. A pico cell would generally cover a relatively smaller coverage area (e.g., buildings) and may allow unrestricted access by UEs 115 with service subscriptions with the network provider. A femto cell would also generally cover a relatively small coverage area (e.g., a home) and, in addition to unrestricted access, may also provide restricted access by UEs 115 having an association with the femto cell (e.g., UEs 115 in a closed subscriber group (CSG), UEs 115 for users in the home, and the like). An eNodeB 105 for a macro cell may be referred to as a macro eNodeB. An eNodeB 105 for a pico cell may be referred to as a pico eNodeB. And, an eNodeB 105 for a femto cell may be referred to as a femto eNodeB or a home eNodeB. An eNodeB 105 may support one or multiple (e.g., two, three, four, and the like) cells.

The core network 130 may communicate with the base stations 105 via a backhaul link 132 (e.g., Si interface, etc.). The base stations 105 may also communicate with one another, e.g., directly or indirectly via backhaul links 134 (e.g., X2 interface, etc.) and/or via backhaul links 132 (e.g., through core network 130). The wireless communications system 100 may support synchronous or asynchronous operation. For synchronous operation, the base stations 105 may have similar frame timing, and transmissions from different base stations 105 may be approximately aligned in time. For asynchronous operation, the base stations 105 may have different frame timing, and transmissions from different base stations 105 may not be aligned in time. The techniques described herein may be used for either synchronous or asynchronous operations.

The mobile devices 115 may be dispersed throughout the wireless communications system 100, and each mobile device 115 may be stationary or mobile. A mobile device 115 may also be referred to by those skilled in the art as a UE, mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless communications device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable terminology. A mobile device 115 may be a cellular phone, a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a tablet computer, a laptop computer, a cordless phone, a wireless local loop (WLL) station, or the like.

The communication links 125 shown in the wireless communications system 100 may include uplink (UL) transmissions from a mobile device 115 to a base station 105, and/or downlink (DL) transmissions, from a base station 105 to a mobile device 115. The downlink transmissions may also be called forward link transmissions while the uplink transmissions may also be called reverse link transmissions.

Mobile device 115 users typically have predictable behavior, often doing the same things or going to the same places at about the same time each day. One example is the travel pattern and schedule of a mobile device 115 user going to and from work. The user may typically leave home at a certain time, travel certain roads to get to work, stay at work until it is time to go back home using the same roads as before, and then repeat more or less the same routine the next day. Because the movements of mobile device 115 user in such a scenario can be foreseeable, it may be possible to predict with a high degree of confidence which cells are used by the mobile device 115 at particular times when going to work, when returning home at the end of the day, or even when taking a lunch break. This prediction may be based on previous measurements, cell reselections (e.g., when the mobile device 115 is in idle mode), and/or handovers, which were performed by the mobile device 115 during the user's commute. Moreover, the use of predictive behavior may also apply to other devices such as laptops, tablets, pads, machine-to-machine (M2M) devices, and the like.

The ability to learn and predict the behavior of the mobile device 115 user may not only be used to reduce the signaling that is needed on the network side, but may also be used to reduce the (usually large) number of measurements of neighboring cells made by the mobile device 115 to determine a suitable cell for handover. Reducing the number of measurements (and related reporting messaging) may have the benefit of extending the battery life of the mobile device 115. In dense urban areas, for example, where large numbers of small cells and/or WiFi hot spots are deployed, predicting the mobile device 115 mobility (e.g., pattern and schedule) may have an impact on the performance of both the network and the mobile device 115.

In addition to the commuting example described above, there may be other instances in which the behavior of the mobile device 115 user may be leveraged to predict cells and reduce network signaling and/or mobile device 115 measurements. One example is when “airplane mode” is turned off after the user arrives at her destination. When such a trip is routine and the behavior predictable, the mobile device 115 may start by identifying a cell at the place of arrival instead of looking (unsuccessfully) for cells at the place of departure as it would typically do.

In yet another example of predictive behavior, when a cell that is typically used by a mobile device during the user's commute to work is congested, the network may look at other cells and may use predictive techniques and the loading levels on the other cells to identify a suitable cell for handover (or cell reselection when the mobile device 115 is in idle mode, e.g., camping on the network). Moreover, when the network knows that the mobile device 115 is going to use a particular cell at a certain time it may try to schedule appropriate resources to be available for the mobile device 115.

In still other examples of predictive behavior, the mobile device 115 may selectively enable or disable one or more air interfaces based on the historical information and/or a current location of the mobile device. In certain examples, the mobile device 115 may disable all air interfaces (e.g., enter airplane mode) when the mobile device 115 follows a known path to a certain location (e.g., the airport) in accordance with historical patterns. Additionally or alternatively, a multi-mode mobile device 115 may selectively disable specific air interfaces (e.g., a WLAN/WiFi interface, an LTE interface, a 1X/EV-DO interface, etc.) based on the historical information and a current location of the mobile device 115.

Generally, predictive mobility in wireless networks may be used to alleviate network signaling demands, to reduce mobile device 115 measurements to extend battery life, and/or to allocate networking resources more effectively, for example.

FIG. 2 shows a diagram of a simplified example of device mobility in a wireless communications system 200, according to one aspect of the principles described herein. In the wireless communications system 200 of FIG. 2, a mobile device 115-a travels along a path 205 through the coverage areas 110-a, 110-b, 110-c, 110-d of a first base station 105-a, a second base station 105-b, a third base station 105-c, and a fourth base station 105-d. The mobile device 115-a may be an example of one or more of the mobile devices 115 of FIG. 1. Similarly, the base stations 105 of FIG. 2 may be examples of one or more of the base stations 105 of FIG. 1.

Each base station 105 may represent an actual or potential serving cell for the mobile device 115-a. In the present example, the mobile device 115-a may begin at position 1 with the first base station 105-a as the serving cell, then move through the coverage area 110-a of the first base station 105-a to position 2. At position 2, the mobile device 115-a may be located at the outer reaches of the coverage area 110-a of the first base station 105-a and enter an intersection of the coverage areas 110-a, 110-b, 110-c of the first, second, and third base stations 105-a, 105-b, 105-c. At position 2, the mobile device 115-a may report a signal strength measurement of the first base station 105-a, the current serving cell, to the first base station 105-a.

In conventional systems, if the mobile device 115-a is in connected mode with the first base station 105-a, the signal strength measurement of the first base station 105-a may indicate that the mobile device 115-a is exiting the coverage area 110-a of the first base station 105-a and trigger preparations for a handover of the mobile device 115-a from the first base station 105-a to a new serving cell base station. Accordingly, the first base station 105-a may instruct the mobile device 115-a to measure the signal strengths of neighboring base stations to identify a handover candidate for the mobile device 115-a. The mobile device 115-a may identify the neighboring base stations 105-b, 105-c using a stored neighboring cell list (NCL) and/or by scanning for the neighboring base stations 105-b, 105-c. If the mobile device 115-a is in idle mode, the mobile device 115-a may measure neighboring cells to identify a reselection target based on a pre-defined threshold for the serving cell signal strength, as configured by the carrier.

The mobile device 115-a may transmit signal strength measurements to the serving base station 105-a, and the serving base station 105-a may select either the second base station 105-b or the third base station 105-c as the handover target base station for the mobile device 115-a based on the signal strength measurements. If the second base station 105-b is selected as the handover target, the mobile device 115-a might briefly handover to the second base station 105-b, and then perform an additional handover to the third base station 105-c as the mobile device 115-a moves out of the coverage area 110-b of the second base station 105-b. In certain examples, upon arriving at position 3, the mobile device 115-a may be handed over to the fourth base station 105-d (e.g., a femtocell or picocell) before returning to the third base station 105-c.

In such systems, it may be difficult for the current serving cell and the mobile device 115-a to determine the optimal time to perform a handover, and the most appropriate handover target. For example, at position 2, a more efficient transition may be for the mobile device 115-a to bypass the second base station 105-b and move directly from the first base station 105-a to the third base station 105-c. Similarly, when the mobile device 115-a is at position 3, the signal strength of the fourth base station 105-d may be stronger than that of the third base station 105-c for a short amount of time, but as the mobile device 115-a is moving along the path 205 (e.g., in a train or automobile), the mobile device 115-a may spend a small amount of time in the coverage area 110-d of the fourth base station 105-d, thereby triggering another handover in short order. In certain examples, the mobile device 115-a may exit the coverage area 110-d of the fourth base station 105-d before there is an opportunity to complete a handover to the next serving cell, which may result in a dropped call or interrupted data connectivity. Thus, it may be more efficient to refrain from handing the mobile device 115-a over to the fourth base station 105-d when it can be determined that the mobile device 115-a is traveling along the path 205.

To address these and other issues, the present description provides methods, systems, and devices that may be used to improve network and/or mobile device 115 performance based on learning and predicting the behavior of the mobile device 115. Using predictive behavior may involve identifying a neighboring cell for measurement by using historical information associated with mobility patterns of the mobile device 115. These measurements may then be used to hand off the mobile device 115 to the identified (i.e., predicted) cell as part of a handover or a cell reselection, for example.

In the example of FIG. 2, for example, the mobile device 115-a may regularly travel along path 205 at regular intervals, times of day, and at consistent speeds. This behavior may be tracked and stored at the mobile device 115-a, a network server, and/or one or more of the base stations 105. Based on the historical information, the mobile device 115-a and/or a current serving base station can predict a next location of the mobile device 115-a, using the predicted next location to inform the selection of handover and reselection targets. For example, when the mobile device 115-a approaches position 2, the first base station 105-a-2 may determine, from the current location and speed of the mobile device 115-a in relation to stored historic data related to mobility patterns of the mobile device 115-a, that the mobile device 115-a is likely traveling along path 205.

Accordingly, the first base station 105-a may determine that the mobile device 115-a is moving more into the coverage area of the third base station 105-c than the second base station 105-b. Based on a level of confidence in that prediction, the base station 105-a may instruct the mobile device 115-a to only measure the signal strength of the third base station 105-c (i.e., rather than all neighboring base stations 105) and, if the signal strength of the third base station 105-c is satisfactory, select the third base station 105-c as the handover target without considering other neighboring base stations 105 as possible handover targets.

Similarly, when the mobile device 115-a approaches position 3 at the fringes of the coverage area 110-c of the third base station 105-c, the third base station 105-c and/or mobile device 115-a may determine, based on the historic data related to the mobility patterns of the mobile device 115-a, that the mobile device 115-a is likely on the known path 205. Accordingly, the third base station 105-c and/or mobile device 115-a may determine that the fourth base station 105-d is an inappropriate handover target for the mobile device 115-a. This determination may be based on the prediction that the mobile device 115-a will continue along path 205, the current speed of the mobile device 115-a, and the known cell edge signal strength of the coverage area 110-d of the fourth base station 105-d. Accordingly, the third base station 105-c and/or the mobile device 115-a may choose to exclude the fourth base station 105-d from signal strength measurements made at the mobile device 115-a to select a handover target.

FIGS. 3A-3C show diagrams examples of device mobility in a wireless communications system 300, according to aspects of the principles described herein. Specifically, FIGS. 3A-3C illustrate an illustrative path 205-a of a mobile device 115-b between a user's home location 305 and the user's work location 310. The path 205-a may traverse the coverage areas 110 of a number of large cells and small cells.

When behavioral information is not considered, the user may travel from the home location 305 to the work location 310 along the depicted path 205-a in a normal manner. For example, when the current serving cell signal strength measurement drops, the mobile device 115-b may notify the network, which in turn may provide a neighboring cell list (NCL) for the mobile device 115-b to take measurements and report the strongest cell.

Referring specifically to FIG. 3A, after the signal strength drops in cell 1, the mobile device 115-b may find cell 2 the strongest and the network may ask the mobile device 115-b to hand-off to cell 2. The same process may take place with cells 3, 4, 5, 6, 7, 8, 9, and 10 until the user reaches the work location 310. Before each handover, however, the mobile device 115-b may make measurements of all the neighboring cells requested by the network even though the same handful of cells is used each day. Moreover, the mobile device 115-b may traverse clusters of femtocells or other small cells (e.g., cells 5, 6, and 10) having small cell radiuses along the path 205-a, which may result in a ping pong effect in which the mobile device 115-b is repeatedly handed over to or from the same set of one or more cells. To overcome these inefficiencies, predictive behavior of the mobile device 115 may be leveraged in a number of ways.

According to a first approach, a predictive algorithm application may reside on the mobile device 115-b. Mobile device profile information (i.e., based on collected historical information associated with mobility patterns of the mobile device) may be stored by the mobile device 115-b for use by the predictive algorithm application. Over a certain learning period (e.g., twenty days), enough information (e.g., location, time, speed, cell measurements, etc.) may be collected by the mobile device 115-b to predict with a high degree of confidence where the mobile device 115 will be on a certain day and time. Alternatively, a network entity (e.g., measurement server) may collect and store the profile information of the mobile device 115-b, and the predictive algorithm application of the mobile device 115-b may communicate with the network entity to access the mobile device profile information.

When the signal strength drops in cell 1 of FIG. 3A, the predictive algorithm application may identify with a high degree of confidence (e.g., >90%) that the mobile device 115-b is moving along a known path 205-a and that the next cell along the path 205-a to the work location 310 is cell 2. The network, not aware of the mobile device 115 behavior, may instruct the mobile device 115-b to make measurements of all neighboring cells in a NCL.

Because of the high degree of confidence that cell 2 is the next cell, the mobile device 115-b may only measure cell 2 and report the measurements of cell 2 to the network before proceeding with a handover to cell 2. Alternatively, the mobile device 115-b may make measurements on a reduced subset of neighboring cells (e.g., a ranked reduced set) found in the NCL. In certain examples, the profile information for the mobile device 115-b may include a history of neighboring cells (e.g., both serving cells and non-serving cells along the path 205-a) for the mobile device 115-b, and the reduced subset of the neighboring cells may be identified based on the history of neighboring cells.

In addition to identifying the reduced subset of the neighboring cells for measurement by the mobile device 115-b, the mobile device 115-b may also select a frequency with which the measurements are made and/or a type of measurement to perform based on the profile information. The types of measurements taken by the mobile device 115-f and determined by the profile information may include serving and neighboring cell radio frequency (RF) measurements, including carrier frequencies, physical cell IDs, location, signal strength measurements (e.g., RSCP, RSRQ, RSRP), time measurements, and the like.

Fewer cells to measure may result in simplified signaling and increased battery life for the mobile device 115-b. In the event the mobile device 115-b does not find the predicted next cell and/or the reduced set of cells, the mobile device 115-b and/or network may fall back to the conventional operation of measuring the full set of neighboring cells. In certain examples, the predicted next cell and/or reduced set of cells may not be found during only a single event, a short period of time, or a deviation from the route. Once the mobile device 115-b and/or network confirms that the mobile device 115-b is back on the known path 205-a, the practice of measuring reduced sets of neighboring cells along the predictive route may continue.

In a different scenario, the mobile device 115-b may be attached to serving cell 1, and the predictive algorithm may determine a confidence level of 60% that cell 2 is the next cell, a confidence level of 20% that cell A is the next cell, and a confidence level of 20% that cell B is the next cell, the mobile device 115-b may elect to make measurements on cell 2, cell A, and cell B as possible handover targets. If cell A or B is the strongest, the mobile device 115-b may operate in its usual mode without taking behavioral information into account. If cell 2 is the strongest, predictive behavior may be used when selecting cells along the path 205-a.

In certain examples, the mobile device may recognize, based on a prediction that the mobile device 115-b will remain on the path 205-a, that certain handovers along the path may be unnecessary. For example, as the mobile device 115-b travels within cell 4, the mobile device 115-a may travel through the coverage areas of cell 5 and cell 6, which may be femtocells. Nevertheless, the mobile device 115-b may determine that handing over to one or more of these femtocells may result in a ping pong effect, a dropped call, or other loss of connectivity due to the small cell radiuses of the femtocells and an estimated amount of time the mobile device 115-b will be in each femtocell. Thus, based on the historical information and current status of the mobile device 115-b, the mobile device 115-b may exclude cell 5 and cell 6 from an identified subset of neighboring cells for which signal strength measurements are to be performed. This decision may result in the mobile device 115-b avoiding handovers to the femtocells while the mobile device 115-b travels along the path 205-a.

In certain examples, where the mobile device 115-b is measuring and storing the signal strength for each cell, the mobile device 115-b may have the ability to create a mean and standard deviation for the signal strength of each cell. The mean and standard deviation values for each cell may allow the mobile device 115-b to remain on a cell or move forward with a hand-over to a target cell when the signal strength of the serving cell is lower than expected. For example, the path 205-a may include a train crossing that occasionally delays travel along the path 205-a. The mobile device 115-b may store or have access to 20 days of historical route information, and during these 20 days a train may have delayed the travel of the mobile device 115-b along the path 205-a 10 times. The train may pass between the mobile device 115-b and the serving cell during this delay, causing the signal strength of the serving cell to drop significantly, even though the mobile device 115-b remains on the predicted path 205-a.

By tracking historical mean and standard deviation values for the serving cell's signal strength, the predictive algorithm application residing on the network and/or the mobile device 115-b may identify that the drop in signal strength is a regular and expected occurrence, thereby allowing the mobile device 115-b to remain connected to the serving cell. In certain examples, the mean and standard deviation values for the signal strength of a particular cell may be used to calculate a quality metric associated with that particular cell for use in handover and reselection decisions.

According to a second approach, the predictive behavior of the mobile device 115-b may be stored in a network entity (e.g., measurement server) and may be accessed by a predictive algorithm in the network to optimize cell measurements and handover procedures. One way in which behavior information may be collected is by tracking the electronic serial number (ESN) or the international subscriber identity (IMSI) through base stations (e.g., NB/eNBs), mobility management entities (MMES), or other network devices. Over the learning period profile information may be collected by the network based on the observed behavior of the mobile device 115-b. The profile information may be used to predict with a high degree of confidence where a particular mobile device 115 will be on a certain day and time.

When the signal strength of a serving cell drops, the predictive algorithm residing on the network may identify with a high degree of confidence (e.g., >90%) the next cell in the path 205-a. The network, instead of providing a full NCL (which may include up to 32 cells for measurements, for example), may instruct the mobile device 115-b to make measurements on a reduced set of cells (e.g., a ranked reduced set) or perhaps only on the predicted next cell. Additionally or alternatively, the network may indicate to the mobile device 115-b that one or more of the neighboring cells have been blacklisted. The mobile device 115-b may then omit the blacklisted neighboring cells. The network may select at least one the blacklisted cells based on the historical profile information associated with the mobile device 115-b. In certain examples, one or more of the cells may be blacklisted when the predictive algorithm determines that the mobile device 115-b is on the path 205-a, but may not be blacklisted when the mobile device 115-b is on other known paths or not traveling on any known path. The mobile device 115-b may report the requested measurements to the network, and the process may proceed with a handover to the predicted next cell.

In a different scenario, the mobile device 115-b may be attached to serving cell 1, and the predictive algorithm may determine a confidence level of 60% that cell 2 is the next cell, a confidence level of 20% that cell A is the next cell, and a confidence level of 20% that cell B is the next cell, the network may instruct the mobile device 115-b to make measurements on those three cells. If cell A or B is the strongest, the network and mobile device 115-b may operate in their usual or conventional mode without taking behavioral information into account. If cell 2 is the strongest, predictive behavior may be used when selecting cells along the path 205-a.

According to a third approach, the predictive behavior of the mobile device 115-b may be stored in a network entity (e.g., measurement server) and may be accessed by both a predictive algorithm application in the mobile device 115-b and a predictive algorithm in the network. When both the mobile device 115-b and the network determine that there is a high degree of confidence that cell 2 is the next cell after the strength of cell 1 drops, the mobile device 115-b and the network may perform a handover to cell 2 without requesting measurements of cell 2 and without reporting measurements of cell 2 (e.g., a blind handover or blindoff).

FIG. 3B shows another example of the path 205-a between the home location 305 and the work location 310. In certain examples, because the mobile device 115-b may store profile information based on collected historical information associated with mobility patterns of the wireless device 115-b, the mobile device 115-b may be able to skip certain cells along the path 205-a that the mobile device 115-b would have otherwise reselected (e.g., based on network defined thresholds) or handed over to (e.g., in response to a handover request message from the network). The mobile device 115-b may elect to skip these cells based on a determination that the value of handing over or reselecting to these cells is limited. For example, the mobile device 115-b may determine that the time that would be spent on a cell would be limited due to ping-ponging between the cell and a neighboring cell. In particular, the mobile device 115-b may perform this skipping during an idle mode, where user plane data is not actively being transmitted or received at the mobile device 115-b.

In some cases, a cell that the mobile device 115-b elects to skip may have a higher received signal strength indicator (RSSI) value than the current cell to which the mobile device 115-b is connected. If the mobile device 115-b elects to skip a cell with a higher RSSI value, the mobile device 115-b may move to a future cell along the path 205-a (which may also have a lower RSSI value than the cell being skipped) or remain connected to the current cell.

In certain examples, the mobile device 115-b may adjust one or more cell measurements along the path 205-a. The cell measurement adjustments may include increasing the cell measurements of one or more cells (e.g., to increase the likelihood of selection as a handover or reselection target) and/or decreasing the measurement of one or more cells (e.g., to decrease the likelihood of selection as a handover or reselection target). For example, if the mobile device 115-b elects to skip a cell along the path 205-a, measurements taken by the mobile device 115-b of that cell may be modified or biased such that a handover to or reselection of that cell does not occur.

Additionally or alternatively, the mobile device 115-b may adjust one or more handover or reselection criterion parameters to affect under what conditions a handover will occur. For example, the RSSI threshold triggering handover or reselection to one or more cells may be increased (e.g., to decrease the likelihood of selection as a handover or reselection target) or decreased (e.g., to increase the likelihood of selection as a handover or reselection target). This modification of RSSI thresholds or other handover or reselection criterion parameters may result in expanded effective coverage areas 320 (indicated by dashed lines) for selected cells along the path 205-a and/or reduced effective coverage areas 320 for other cells along the path 205-a.

These modifications to the cell measurement adjustments and/or the handover or reselection criterion parameters may be chosen and enforced by the network, the mobile device 115-b, or both.

In the example of FIG. 3B, the mobile device 115-b may, based on the historical mobility patterns of the wireless device 115-b, determine that cells 1, 3, 7, and 9 may provide complete coverage to the mobile device 115-b along the path 205-a if the lower RSSI thresholds for handover and reselection are applied to these cells. As shown in FIG. 3B, the expanded effective coverage areas 320 of cells 1, 3, 7, and 9 may cover the entire path 205-a between the home location 305 and the work location 310. This reduction in the number of cells that the mobile device 115-b connects to along the path 205-a may accordingly reduce the number of measurements taken by the mobile device 115-b and handovers to new cells, thereby reducing power consumption and increasing the battery life of the mobile device 115-b and reducing the networking signaling, especially network signaling associated with handovers.

FIG. 3C shows another example of the path 205-a between the home location 305 and the work location 310. In certain examples, distinctive physical features of a given area may shape the coverage area of a cell. For example, a street in a large urban area lined with tall buildings may allow for enhanced propagation of radio frequency signals along the corridor of the street. As shown in FIG. 3C, for example, when taking into consideration a lower RSSI threshold for handover and reselection, the expanded effective coverage area 320 of cell 7 may be long and narrow along a street. If the path 205-a runs along that street, the mobile device 115-b may take advantage of the expanded effective coverage areas 320 of cells 1, 7, and 9 to travel from the home location 305 to the work location 310 by connecting to only cells 1, 7, and 9. The mobile device 115-b may elect to skip other cells traversed by the path 205-a based on a determination that the mobile device 115-b is traveling along the path 205-a and that the value of handing over or reselecting to these cells is limited.

FIG. 4 shows a diagram of another example of communications between devices associated with a handover in a wireless communications system 400, according to one aspect of the principles described herein. The wireless communications system 400 of the present example includes a mobile device 115-c, a base station 105-d associated with a first cell (“cell 1”), a base station 105-e associated with a second cell (“cell 2”), and an optional measurement server 401 configured to store historical information 405 for the mobile device 115-c related to a mobility profile of the mobile device 115-c. In certain embodiments, the mobility profile of the mobile device may be stored entirely on the mobile device 115-c. The wireless communications system 400 may be an example of one or more of the wireless communications systems 100, 200, 300 described above with respect to the previous Figures.

In the present example, a predictive algorithm application 410 may be executed by the base station 105-d of cell 1. The predictive algorithm application 410-a of the mobile device 115-c may store and retrieve historical information associated with mobility patterns of the mobile device 115-c. As described above, the historical information may be entirely stored on the mobile device 115-c. Alternatively, the predictive algorithm application 410 of the mobile device 115-c may retrieve the historical information associated with mobility patterns of the mobile device 115-c from the mobile device 115-c. The mobile device 115-c may enter into a connected mode 412 with cell 1.

At block 415, the mobile device 115-c may measure the signal strength of cell 1. The mobile device 115-c may transmit a report 420 to the base station 105-d of cell 1 indicating that the received signal strength indication (RSSI) of cell 1 is low (e.g., below a threshold). Based on the historical information associated with the mobile device 115-c, the base station 105-d of cell 1 may determine that a handover is imminent and transmit a request 425 to the mobile device 115-c to measure the signal strength of a neighboring cell list (NCL) containing neighboring cells 2, A, and B.

Based on the historical information associated with the mobile device 115-c, the predictive algorithm application 410 of the mobile device 115-c may determine with a confidence level greater than a threshold (e.g., 90%) that the next cell for the mobile device 115-c is cell 2. The mobile device 115-c may therefore choose to measure the signal strengths of only cell 1 and cell 2 at block 430, transmit a report 435 to the base station 105-d of cell 1 of the signal strength of cell 2, and the base station 105-d of cell 1 may initiate a handover of the mobile device 115-c to cell 2. The base station 105-d of cell 1 may then work with the mobile device 115-c and the base station 105-e of cell 2 to handover 440 the mobile device 115-c from cell 1 to cell 2, and the mobile device 115-c may enter a connected mode 445 with cell 2.

FIG. 5 shows a diagram of an example of communications between devices associated with a handover in a wireless communications system 500, according to one aspect of the principles described herein. The wireless communications system 500 of the present example includes a mobile device 115-d, a base station 105-f associated with a first cell (“cell 1”), a base station 105-g associated with a second cell (“cell 2”), and a measurement server 401-a configured to store historical information for the mobile device 115-d related to a mobility profile of the mobile device 115-d. The wireless communications system 500 may be an example of one or more of the wireless communications systems 100, 200, 300, 400 described above with respect to the previous Figures.

In the present example, a predictive algorithm application 410-a is executed by the base station 105-f of cell 1. The base station 105-f of cell 1 may communicate with the measurement server 401-a to store and retrieve historical information 515 associated with mobility patterns of the mobile device 115-d. The mobile device 115-d may enter into a connected mode 520 with cell 1.

At block 525, the mobile device 115-d may measure the signal strength of cell 1, the serving cell. The mobile device 115-d may transmit a report 530 to the base station 105-f of cell 1 indicating that the received signal strength indication (RSSI) of cell 1 is low (e.g., below a threshold). Based on the historical information associated with the mobile device 115-d, the base station 105-f of cell 1 may use the predictive algorithm application 410-a to determine with a confidence level greater than a threshold (e.g., 90%) that the next cell for the mobile device 115-d is cell 2. Accordingly, the base station 105-f of cell 1 may transmit a request 535 to the mobile device 115-d to measure the signal strength of a neighboring cell list (NCL) containing only cell 2. The base station 105-f may exclude all other neighboring cells from the NCL.

The mobile device 115-d may measure the signal strengths of cell 1 and cell 2 at block 540, transmit a report 545 to the base station 105-f of cell 1 that the signal strength of cell 2 is greater than a threshold level, and the base station 105-f of cell 1 may initiate a handover of the mobile device 115-d to cell 2. The base station 105-f of cell 1 may then work with the mobile device 115-d and the base station 105-g of cell 2 to handover 550 the mobile device 115-d from cell 1 to cell 2, and the mobile device 115-d may enter a connected mode 555 with cell 2.

FIG. 6 shows a diagram of another example of communications between devices associated with a handover in a wireless communications system 600, according to one aspect of the principles described herein. The wireless communications system 600 of the present example includes a mobile device 115-e, a base station 105-h associated with a first cell (“cell 1”), a base station 105-i associated with a second cell (“cell 2”), and a measurement server 401-b configured to store historical information for the mobile device 115-e related to a mobility profile of the mobile device 115-e. The wireless communications system 600 may be an example of one or more of the wireless communications systems 100, 200, 300, 400, 500 described above with respect to the previous Figures.

In the present example, predictive algorithm applications 410 are executed by both the base station 105-h of cell 1 and the mobile device 115-e. The predictive algorithm applications 410 may communicate with the measurement server 401-b to store and retrieve historical information 605, 610 associated with mobility patterns of the mobile device 115-e. The mobile device 115-e may enter into a connected mode 615 with cell 1.

At block 620, the mobile device 115-e may measure the signal strength of cell 1. The mobile device 115-e may transmit a report 625 to the base station 105-h of cell 1 indicating that the received signal strength indication (RSSI) of cell 1 is low (e.g., below a threshold). Based on the historical information associated with the mobile device 115-e, the predictive algorithm applications 410 of the base station 105-h and the mobile device 115-e may determine that a handover is imminent and that, based on a predictive analysis of the historical information related to the mobility profile of the mobile device 115-e, that the cell 2 is the next cell for the mobile device 115-e. Accordingly, the base station 105-h of cell 1 may work with the mobile device 115-e and the base station 105-i of cell 2 to perform a blind handover 630 the mobile device 115-e from cell 1 to cell 2, and the mobile device 115-e may enter a connected mode 635 with cell 2.

FIG. 7 shows a block diagram of a wireless communications system 700, according to one aspect of the principles described herein. The wireless communications system 700 may include an operations, administration and management (OAM) system 705, a minimize drive test (MDT) server 401-c, a base station 105-j, and a mobile device 115-E Each of these components may be in communication, directly or indirectly. The wireless communications system 700 may include aspects of one or more of the wireless communications systems 100, 200, 300, 400, 500, 600 described above with reference to the previous Figures.

The MDT server 401-c may be a server used by the network to gather information used by network operators in evaluating network performance. The MDT server 401-c may be an example of one or more of the measurement servers 401 described above with reference to FIGS. 4-6. Using the MDT server 401-c, measurements taken at a mobile device 115-f in connected mode (immediate MDT) or idle mode (logged MDT) may be requested from the mobile device 115-E The types of measurements taken by the mobile device 115-f and requested by the MDT server 401-c may include serving and neighboring cell radio frequency (RF) measurements, including carrier frequencies, physical cell IDs, location, signal strength measurements (e.g., RSCP, RSRQ, RSRP), time measurements, and the like.

The controller for the MDT functionality may reside in the OAM system 705 of the network. The OAM system 705 may initiate and control the MDT data collection processes by sending a message activating the measurements and also including parameters for the data collection to the base station 105-j. The base station 105-j may then pass on the message to the mobile device 115-E After the measurements are completed by the mobile device 115-f, the mobile device 115-f may transmit the collected measurements to the base station 105-j, and these measurements may then be forwarded to the MDT server 401-c for storage and processing.

One or more MDT servers 401-c deployed in a network may be used to store historical information for the mobile device 115-f associated with mobility patterns of the mobile device 115-f. The historical information for the mobile device 115-f may be gathered and transmitted to the MDT server 401-c for storage. The historical information may then be used by a predictive algorithm application 410-d running on the base station 105-j (as shown in FIG. 7) and/or the mobile device 115-f (not shown) to identify a subset of a set of neighboring cells for measurement by the mobile device 115-f consistent with the foregoing principles. While the base station 105-j of FIG. 7 is shown running a predictive algorithm application 410-d, it will be understood that one or more of the base stations 105 gathering the historical information for the mobile device 115-f may not be running the predictive algorithm application 410-d.

FIG. 8 shows a block diagram of a wireless communications system 800, according to one aspect of the principles described herein. Specifically, FIG. 8 illustrates a design of a base station 105-k and a mobile device 115-g, in accordance with an aspect of the present disclosure. The wireless communications system 800 may illustrate aspects of one or more of the wireless communications systems 100, 200, 300, 400, 500, 600, 700 described above with reference to previous Figures. For example, the base station 105-k may be an example of one or more of the base stations 105 described above with respect to FIGS. 1-7, and the mobile device 115-g may be an example of one or more of the mobile devices 115 described above with respect to FIGS. 1-7.

The base station 105-k may be equipped with base station antennas 834-1 through 834-x, where x is a positive integer, and the mobile device 115-g may be equipped with mobile device antennas 852-1 through 852-n, where n is a positive integer. In the wireless communications system 800, the base station 105-k may be able to send data over multiple communication links at the same time. Each communication link may be called a “layer” and the “rank” of the communication link may indicate the number of layers used for communication. For example, in a 2×2 MIMO system where base station 105-k transmits two “layers,” the rank of the communication link between the base station 105-k and the mobile device 115-g is two.

At the base station 105-k, a base station transmit processor 820 may receive data from a base station data source and control information from a base station controller/processor 840. The control information may be for the PBCH, PCFICH, PHICH, PDCCH, etc. The data may be for the PDSCH, etc. The base station transmit processor 820 may process (e.g., encode and symbol map) the data and control information to obtain data symbols and control symbols, respectively. The base station transmit processor 820 may also generate reference symbols, e.g., for the PSS, SSS, and cell-specific reference signal. An base station transmit (TX) MIMO processor 830 may perform spatial processing (e.g., precoding) on data symbols, control symbols, and/or reference symbols, if applicable, and may provide output symbol streams to the base station transmit modulators 832-1 through 832-x. Each base station modulator 832 may process a respective output symbol stream (e.g., for OFDM, etc.) to obtain an output sample stream. Each base station modulator 832 may further process (e.g., convert to analog, amplify, filter, and upconvert) the output sample stream to obtain a downlink (DL) signal. In one example, DL signals from base station modulators 832-a through 832-x may be transmitted via the base station antennas 834-a through 834-x, respectively.

At the mobile device 115-g, the mobile device antennas 852-1 through 852-n may receive the DL signals from the base station 105-k and may provide the received signals to the mobile device demodulators 854-a through 854-n, respectively. Each mobile device demodulator 854 may condition (e.g., filter, amplify, downconvert, and digitize) a respective received signal to obtain input samples. Each mobile device demodulator 854 may further process the input samples (e.g., for OFDM, etc.) to obtain received symbols. A mobile device MIMO detector 856 may obtain received symbols from all the demodulators 854-a through 854-n, perform MIMO detection on the received symbols if applicable, and provide detected symbols. A mobile device receiver (Rx) processor 858 may process (e.g., demodulate, deinterleave, and decode) the detected symbols, providing decoded data for the mobile device 115-g to a data output, and provide decoded control information to a mobile device processor 880, or mobile device memory 882.

On the uplink (UL), at the mobile device 115-g, a mobile device transmit processor 864 may receive and process data from a mobile device data source. The mobile device transmit processor 864 may also generate reference symbols for a reference signal. The symbols from the mobile device transmit processor 864 may be precoded by a mobile device transmit MIMO processor 866 if applicable, further processed by the mobile device demodulators 854-a through 854-n (e.g., for SC-FDMA, etc.), and be transmitted to the base station 105-k in accordance with the transmission parameters received from the base station 105-k. At the base station 105-k, the UL signals from the mobile device 115-g may be received by the base station antennas 834, processed by the base station demodulators 832, detected by a base station MIMO detector 836 if applicable, and further processed by a base station receive processor 838. The base station receive processor 838 may provide decoded data to a base station data output and to the base station processor 840.

The components of the mobile device 115-g may, individually or collectively, be implemented with one or more Application Specific Integrated Circuits (ASICs) adapted to perform some or all of the applicable functions in hardware. Each of the noted modules may be a means for performing one or more functions related to operation of the wireless communications system 800. Similarly, the components of the base station 105-k may, individually or collectively, be implemented with one or more Application Specific Integrated Circuits (ASICs) adapted to perform some or all of the applicable functions in hardware. Each of the noted components may be a means for performing one or more functions related to operation of the wireless communications system 800.

The communication networks that may accommodate some of the various disclosed embodiments may be packet-based networks that operate according to a layered protocol stack. For example, communications at the bearer or Packet Data Convergence Protocol (PDCP) layer may be IP-based. A Radio Link Control (RLC) layer may perform packet segmentation and reassembly to communicate over logical channels. A Medium Access Control (MAC) layer may perform priority handling and multiplexing of logical channels into transport channels. The MAC layer may also use Hybrid ARQ (HARQ) to provide retransmission at the MAC layer to improve link efficiency. At the Physical layer, the transport channels may be mapped to Physical channels.

In one configuration, the base station 105-k may operate as a serving base station 105-k for the mobile device 115-g, and may include means for identifying a set of neighboring cells for measurement by the mobile device 115-g, where the identification is based on historical information associated with mobility patterns of the mobile device 115-g. In one aspect, the aforementioned means may be the base station controller/processor 840, the base station memory 842, the base station transmit processor 820, base station receiver processor 838, the base station modulators/demodulators 832, and the base station antennas 834 of the base station 105-k configured to perform the functions recited by the aforementioned means.

In an additional or alternative configuration, the mobile device 115-g may include means for identifying a set of neighboring cells for measurement by the mobile device 115-g, where the identification is based on historical information associated with mobility patterns of the mobile device 115-g. In one aspect, the aforementioned means may be the mobile device controller/processor 880, the mobile device memory 882, the mobile device transmit processor 864, mobile device receiver processor 858, the mobile device modulators/demodulators 854, and the mobile device antennas 852 configured to perform the functions recited by the aforementioned means.

FIG. 9 shows a block diagram of one example of a mobile device 115-h, according to one aspect of the principles described herein. The mobile device 115-h may be an example of one or more of the mobile devices 115 described above with reference to the previous Figures.

The mobile device 115-h of FIG. 9 may include a processor 910, a memory 915, a prediction module 920, a cell measurement module 925, a handover module 930, and a wireless wide area network (WWAN) radio 950. Each of these components may be in communication, directly or indirectly.

The processor 910 may be configured to execute computer-readable program code stored by the memory 915 to implement one or more aspects of the prediction module 920, the cell measurement module 925, the handover module 930, and/or the wireless wide area network (WWAN) radio 950. The processor 910 may also execute computer-readable program code stored by the memory 915 to implement other applications 917.

The prediction module 920 may be configured to implement the functionality of one or more of the predictive algorithm applications 410 described above with respect to the previous Figures. In certain examples, the prediction module 920 may identify a subset of a set of neighboring cells for measurement by the mobile device 115-h based on historical information 919 associated with mobility patterns of the mobile device 115-h. The subset may further be identified based on a current location or state of the mobile device 115-h in relation to the historical information 919. Additionally or alternatively, the prediction module 920 may identify, based on the historical information 919, an order in which measurements of neighboring cells are to be performed (e.g., according to the likelihood of being the next cell). In additional or alternative examples, the prediction module 920 may identify how frequently measurements of neighboring cells are performed and/or the type of measurements to take.

In certain examples, a serving cell of the mobile device 115-h (e.g., a cell associated with one or more of the base stations 105 described in other Figures of the present disclosure) may identify the subset of the neighboring cells based on the historical information 919, the order of measurements, the frequency of measurements, and/or the type of measurements as described above. In this case, the prediction module 920 may determine this information based on signaling from the serving cell. The mobile device 115-h may communicate with the serving cell using the WWAN radio 950. In certain examples, the prediction module 920 may communicate with a server (e.g., over WWAN radio 950) to receive the historical information 919. Additionally or alternatively, the mobile device 115-h may collect and store the historical information 919 locally in the memory 915 of the mobile device 115-h, as shown in FIG. 9.

The historical information may include information about the mobility patterns of the mobile device 115-h. The mobility patterns may include, for example, a route and a schedule of the mobile device 115-h between a first location and a second location. Additionally or alternatively, the mobility patterns may include a location and a period of time during which the mobile device 115-h remains at the location. Thus, in certain examples, the historical information may include a serving cell history of the mobile device 115-h over a predetermined period of time, as observed and stored by the server, the serving cell, and/or the mobile device 115-h.

The cell measurement module 925 may be configured to perform signal strength measurements on the one or more neighboring cells according to the determinations made by the prediction module 920. For example, the cell measurement module may make measurements of an identified subset of the neighboring cells and report the signal strength measurements to the serving cell. The handover module 930 may be configured to perform a handover or reselection of the mobile device 115-h to a target cell in the identified subset. In certain examples, the serving cell may select the target cell from the identified subset based on the signal strength measurements provided by the cell measurement module 925. The serving cell may then indicate the selected target cell to the mobile device 115-h through WWAN signaling. Additionally or alternatively, the mobile device 115-h may perform or aid in the selection of the target cell based on the signal strength measurements for the identified subset.

In certain examples, the identified subset may include a single neighboring cell. In such cases, the cell measurement module 925 may measure a signal strength associated with the single neighboring cell, and the handover module 930 may perform a handover (initiated by the mobile device 115-h or the serving cell) of the mobile device 115-h to the single neighboring cell if the signal strength of the single neighboring cell is greater than a threshold level. Accordingly, the prediction module 920 and cell measurement module 925 may determine not to perform signal strength measurements of neighboring cells other than the single neighboring cell when the signal strength of the single neighboring cell is greater than a threshold level.

In certain examples, the prediction module 920 may determine a quality metric for each of the neighboring cells in the set, and the subset may be selected to include each neighboring cell that has a quality metric greater than a threshold level. The quality metric may be based on, for example, the signal strength of each neighboring cell, a data rate associated with the neighboring cell, an ability of the neighboring cell to perform offloading to an alternate radio access technology, a projected amount of time for which the mobile device 115-h will remain connected to the neighboring cell, and/or other relevant factors. In certain examples, known mean and standard deviation values of the signal strength for the cell may influence the effect of the signal strength on the quality metric for that cell. In certain examples, the prediction module 920 or the serving cell may rank the neighboring cells in the subset according to their respective quality metrics.

In certain examples, the prediction module 920 may base the quality metric of each neighboring cell on a confidence level. The confidence level for each neighboring cell may indicate a level of confidence, based on the historical information 919 for the mobile device 115-h, that the neighboring cell will be the next cell in a mobility path of the mobile device 115-h. In such examples, the identified subset of the neighboring cells may include the neighboring cells having a confidence level greater than a threshold level.

In certain examples, the subset of the neighboring cells identified for measurement may include neighboring cells with a confidence level greater than a first threshold (35%). Out of the identified subset, the prediction module 920 and/or serving cell may determine that one of the neighboring cells in the subset has a confidence level greater than a second, higher threshold (e.g., 90%). In that case, the mobile device 115-h may communicate with the serving cell to perform a blind handover (e.g., a handover without measurements) to the neighboring cell having the confidence level higher than the second threshold.

In certain examples, the prediction module 920 and/or the serving cell for the mobile device 115-h may exclude one or more of the neighboring cells from the subset selected for measurements based at least on a current speed of the mobile device 115-h and a signal strength of the one or more neighboring cells. For example, if the mobile device 115-h is traveling along a known path and momentarily passes through the coverage area of a femtocell while the subset of the neighboring cells selected for measurement is identified, the femtocell may be excluded from the subset of neighboring cells selected for measurement due to the likelihood that the mobile device 115-h will soon be outside the coverage area of the femtocell.

FIG. 10 shows a block diagram of one example of a base station 105-1, according to one aspect of the principles described herein. The base station 105-1 may be an example of one or more of the base stations 105 described above with reference to the previous Figures. The base station 105-1 may be associated with a serving cell of one or more of the mobile devices 115 described above with reference to the previous Figures.

The base station 105-1 of FIG. 10 may include a processor 910-a, a memory 915-a, a prediction module 920-a, a cell measurement requesting module 1025, a handover module 930-a, a wireless wide area network (WWAN) radio 950-a, and a backhaul/core network interface 1055. Each of these components may be in communication, directly or indirectly.

The processor 910-a may be configured to execute computer-readable program code stored by the memory 915-a to implement one or more aspects of the prediction module 920-a, the cell measurement requesting module 1025, the handover module 930-a, the WWAN radio 950-a, and/or the backhaul/core network interface 1055. The processor 910-a may also execute computer-readable program code stored by the memory 915-a to implement other applications 917-a.

The prediction module 920-a may be configured to identify a subset of a set of neighboring cells for measurement by a mobile device (e.g., one or more of the mobile devices 115 described in the present disclosure) based on historical information 919-a associated with mobility patterns of the mobile device. The subset may further be identified based on a current location or state of the mobile device in relation to the historical information 919. In certain examples, the base station 105-1 may select the subset of the neighboring cells based on the historical information 919-a and location or state of the mobile device. The base station 105-1 may then signal (e.g., using the WWAN radio 950-a) the identified subset of the neighboring cells to the mobile device. Additionally or alternatively, a separate network entity (e.g., a server or mobility management entity (MME)) may select the subset of the neighboring cells based on the historical information 919-a and location or state of the mobile device and signal (e.g., over the backhaul/core network interface 1055) the selected subset of the neighboring cells to the base station 105-1 for forwarding to the mobile device. In still other examples, the mobile device may itself select the subset of the neighboring cells based on the historical information 919-a.

Additionally or alternatively, the prediction module 920-a may identify, based on the historical information 919-a, an order in which measurements of neighboring cells are to be performed (e.g., according to likelihood of being the next cell) by the mobile device. In additional or alternative examples, the prediction module 920-a may identify a frequency with which measurements of neighboring cells are performed and/or the type of measurements to take.

In examples where the base station 105-1 determines the subset of the neighboring cells to the mobile device for measurement by the mobile device, the base station 105-1 may collect and store the historical information 919-a locally in the memory 915-a of the base station 105-1, as shown in FIG. 10.

The historical information may include information about the mobility patterns of the mobile device. The mobility patterns may include, for example, a route and a schedule of the mobile device between a first location and a second location. Additionally or alternatively, the mobility patterns may include a location and a period of time during which the mobile device remains at the location. Thus, in certain examples, the historical information may include a serving cell history of the mobile device over a predetermined period of time, as collected by a network server, the base station 105-1, and/or the mobile device 115-f.

The cell measurement requesting module 1025 may be configured to instruct the mobile device to perform signal strength measurements on the one or more neighboring cells in the identified subset and report the signal strength measurements to the base station 105-1. The handover module 930-a may be configured to perform a handover or reselection of the mobile device to a target cell in the identified subset. In certain examples, the handover module 930-a of the base station 105-1 may select the target cell from the identified subset based on the signal strength measurements at the mobile device provided in response to the request made by the cell measurement requesting module 1025. The handover module 930-a of the base station 105-1 may indicate the selected target cell to the mobile device through WWAN signaling. Additionally or alternatively, the mobile device may perform or aid in the selection of the target cell based on the signal strength measurements for the identified subset.

In certain examples, the identified subset may include a single neighboring cell. In such cases, the cell measurement requesting module 1025 may request signal strength measurements for the single neighboring cell, and the handover module 930-a may perform a handover (initiated by the mobile device or the base station) of the mobile device to the single neighboring cell if the signal strength of the single neighboring cell is greater than a threshold level. Accordingly, the prediction module 920-a and cell measurement requesting module 1025 may determine not to request signal strength measurements of neighboring cells other than the single neighboring cell when the signal strength of the single neighboring cell is greater than a threshold level.

In certain examples, the prediction module 920-a may determine a quality metric for each of the neighboring cells in the set, and the subset may be selected to include each neighboring cell that has a quality metric greater than a threshold level. The quality metric may be based on, for example, the signal strength of each neighboring cell, a data rate associated with the neighboring cell, an ability of the neighboring cell to perform offloading to an alternate radio access technology, a projected amount of time for which the mobile device will remain connected to the neighboring cell, and/or other relevant factors. In certain examples, the prediction module 920-a or the serving cell may rank the neighboring cells in the subset according to their respective quality metrics.

In certain examples, the prediction module 920-a may base the quality metric of each neighboring cell on a confidence level. The confidence level for each neighboring cell may indicate a level of confidence, based on the historical information 919-a for the mobile device, that the neighboring cell will be the next cell in a mobility path of the mobile device. In such examples, the identified subset of the neighboring cells may include the neighboring cells having a confidence level greater than a threshold level.

In certain examples, the subset of the neighboring cells identified for measurement may include neighboring cells with a confidence level greater than a first threshold (35%). Out of the identified subset, the prediction module 920-a and/or mobile device may determine that one of the neighboring cells in the subset has a confidence level greater than a second, higher threshold (e.g., 100%). In that case, the base station 105-1 may communicate with the mobile device to perform a blind handover (e.g., a handover without measurements) to the neighboring cell having the confidence level higher than the second threshold.

In certain examples, the prediction module 920-a and/or the mobile device may exclude one or more of the neighboring cells from the subset selected for measurements based at least on a current speed of the mobile device and a signal strength of the one or more neighboring cells. For example, if the mobile device is traveling along a path and momentarily passes through the coverage area of a femtocell while the subset of the neighboring cells selected for measurement is identified, the femtocell may be excluded from the subset of neighboring cells selected for measurement due to the likelihood that the mobile device will soon be outside the coverage area of the femtocell.

FIG. 11 shows a flowchart diagram of a method 1100 for managing wireless communications, in accordance with an aspect of the present disclosure. Specifically, FIG. 11 illustrates a method 1100 of improving network and/or mobile device performance based on learning and predicting the behavior of a mobile device. The method 1100 may be implemented in one or more of the wireless communications systems 100, 200, 300, 400, 500, 600, 700, 800 described above with respect to the previous Figures. In particular, the method 1100 may be performed by one or more of the base stations 105, mobile devices 115, or other nodes described above with reference to the previous Figures.

At block 1105, historical information associated with mobility patterns of a mobile device may be received. The historical information may be received by collecting and storing the historical information and/or by receiving the historical information from another device. At block 1110, a subset of neighboring cells of the mobile device may be identified for measurement by the mobile device. The subset of neighboring cells may be identified for measurement based on the historical information. Additionally or alternatively, the historical information may be used to identify or determine an order in which measurements of neighboring cells are to be performed (e.g., according to likelihood of being the next cell), a frequency with which measurements of neighboring cells are performed, and/or the type of measurements to take.

FIG. 12 shows a flowchart diagram of a method 1200 for managing wireless communications, in accordance with an aspect of the present disclosure. Specifically, FIG. 12 illustrates a method 1200 of improving network and/or mobile device performance based on learning and predicting the behavior of a mobile device. The method 1200 may be implemented in one or more of the wireless communications systems 100, 200, 300, 400, 500, 600, 700, 800 described above with respect to the previous Figures. In particular, the method 1200 may be performed by one or more of the base stations 105 or other network devices described above with reference to the previous Figures.

At block 1205, historical information associated with mobility patterns of a mobile device may be received from a server. The historical information may include a serving cell history for the mobile device. At block 1210, a set of neighboring cells for the mobile device may be identified. The set of neighboring cells may be identified based on a location of the mobile device and/or a neighboring cell list maintained by the mobile device and/or the current serving cell of the mobile device.

At block 1215, a confidence level may be determined for each of the neighboring cells of the mobile device. The confidence level of each neighboring cell may indicate a likelihood that that neighboring cell will be the next cell in a current mobility path of the mobile device. At block 1220, a subset of the neighboring cells of the mobile device having a confidence level greater than a threshold level may be identified. At block 1225, the mobile device may be instructed to measure the signal strength of the subset of the neighboring cells. At block 1230, a handover target for the mobile device may be selected based on the measured signal strengths. In additional or alternative embodiments, all of the actions of blocks 1205 through 1230 may be performed by the mobile device (e.g., when the mobile device is in idle mode). In these cases, the mobile device may make the decision of the initial cells to measure based on pre-defined rules and parameters of the network, taking into account the historical information. In such embodiments the mobile device may select a reselection target instead of a handover target at block 1230.

FIG. 13 shows a flowchart diagram of a method 1300 for managing wireless communications, in accordance with an aspect of the present disclosure. Specifically, FIG. 13 illustrates a method 1300 of improving network and/or mobile device performance based on learning and predicting the behavior of a mobile device. The method 1300 may be implemented in one or more of the wireless communications systems 100, 200, 300, 400, 500, 600, 700, 800 described above with respect to the previous Figures. In particular, the method 1300 may be performed by one or more of the mobile devices 115 or other network devices described above with reference to the previous Figures.

At block 1305, a serving cell signal strength, measured at a mobile device, may be reported to the serving cell. At block 1310, an instruction may be received from the serving cell to measure the signal strength of a subset of neighboring cells of the mobile device. The subset may be identified by the serving cell or the mobile device based on historical information associated with mobility patterns of the mobile device. The historical information may include a serving cell history for the mobile device. At block 1315, signal strength measurements for the identified subset of the neighboring cells may be transmitted to the serving cell. At block 1320, a handover for the mobile device may be performed with a target cell selected from the subset of the neighboring cells based on the signal strength measurements.

FIG. 14 shows a flowchart diagram of a method 1400 for managing wireless communications, in accordance with an aspect of the present disclosure. Specifically, FIG. 14 illustrates a method 1400 of improving network and/or mobile device performance based on learning and predicting the behavior of a mobile device. The method 1400 may be implemented in one or more of the wireless communications systems 100, 200, 300, 400, 500, 600, 700, 800 described above with respect to the previous Figures. In particular, the method 1400 may be performed by one or more of the mobile devices 115 or other network devices described above with reference to the previous Figures.

At block 1405, a mobile device in connected mode may report a serving cell signal strength to the serving cell. At block 1410, the mobile device may receive an instruction from the serving cell to measure the signal strength of all neighboring cells. At block 1415, the mobile device may identify a subset of the neighboring cells based on historical information associated with mobility patterns of the mobile device. The historical information may include a serving cell history for the mobile device. At block 1420, signal strength measurements for only the identified subset of the neighboring cells may be transmitted to the serving cell from the mobile device. At block 1425, a handover target may be selected for the mobile device based on the measured signal strengths.

FIG. 15 shows a flowchart diagram of a method 1500 for managing wireless communications, in accordance with an aspect of the present disclosure. Specifically, FIG. 15 illustrates a method 1500 of improving network and/or mobile device performance based on learning and predicting the behavior of a mobile device. The method 1500 may be implemented in one or more of the wireless communications systems 100, 200, 300, 400, 500, 600, 700, 800 described above with respect to the previous Figures. In particular, the method 1500 may be performed by one or more of the mobile devices 115 or other network devices described above with reference to the previous Figures.

At block 1505, a mobile device in idle mode may receive an instruction from a serving cell to measure the signal strength of all neighboring cells. This instruction may be received as a broadcast message in one or more system information blocks from the serving cell. At block 1510, the mobile device may identify a subset of the neighboring cells based on historical information associated with mobility patterns of the mobile device. The historical information may include a serving cell history for the mobile device. At block 1515, the mobile device may perform signal strength measurements for the subset of the neighboring cells. At block 1520, a cell reselection target may be selected for the mobile device from the subset based on the measured signal strengths.

FIG. 16 shows a flowchart diagram of a method 1600 for managing wireless communications, in accordance with an aspect of the present disclosure. Specifically, FIG. 16 illustrates a method 1600 of improving network and/or mobile device performance based on learning and predicting the behavior of a mobile device. The method 1600 may be implemented in one or more of the wireless communications systems 100, 200, 300, 400, 500, 600, 700, 800 described above with respect to the previous Figures. In particular, the method 1600 may be performed by one or more of the mobile devices 115 or other network devices described above with reference to the previous Figures.

At block 1605, the mobile device may report to a serving cell a signal strength measurement of the serving cell. The signal strength of the serving cell may indicate an imminent handover of the mobile device. At block 1610, a subset of neighboring cells of the mobile device may be identified for measurement by the mobile device based on historical information associated with mobility patterns of the mobile device. The historical information may include a serving cell history for the mobile device. The target handover cell may be identified based on a determination that a confidence level of the target handover cell is greater than a threshold level. At block 1615, the mobile device may communicate with the serving cell to coordinate a blind handover of the mobile device to the identified target handover cell without performing signal strength measurements for any of the neighboring cells.

The detailed description set forth above in connection with the appended drawings describes exemplary embodiments and does not represent the only embodiments that may be implemented or that are within the scope of the claims. The term “exemplary” used throughout this description means “serving as an example, instance, or illustration,” and not “preferred” or “advantageous over other embodiments.” The detailed description includes specific details for the purpose of providing an understanding of the described techniques. These techniques, however, may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described embodiments.

Information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

The various illustrative blocks and modules described in connection with the disclosure herein may be implemented or performed with a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, due to the nature of software, functions described above can be implemented using software executed by a processor, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. Also, as used herein, including in the claims, “or” as used in a list of items prefaced by “at least one of” indicates a disjunctive list such that, for example, a list of “at least one of A, B, or C” means A or B or C or AB or AC or BC or ABC (i.e., A and B and C).

Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium may be any available medium that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-Ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above are also included within the scope of computer-readable media.

The previous description of the disclosure is provided to enable a person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Throughout this disclosure the term “example” or “exemplary” indicates an example or instance and does not imply or require any preference for the noted example. Thus, the disclosure is not to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. 

What is claimed is:
 1. A method for managing wireless communications, comprising: identifying a subset of a set of neighboring cells for measurement by a mobile device, the identification based on historical information associated with mobility patterns of the mobile device.
 2. The method of claim 1, wherein the method is performed by a network entity, the mobile device, or a combination of the two.
 3. The method of claim 1, further comprising: receiving the historical information from a server, the historical information comprising a serving cell history of the mobile device over a predetermined period of time collected by the server.
 4. The method of claim 3, wherein the historical information further comprises a history of neighboring cells for the mobile device.
 5. The method of claim 1, further comprising: collecting and storing at the mobile device a serving cell history of the mobile device over a predetermined period of time, the historical information comprising the serving cell history.
 6. The method of claim 5, further comprising: collecting and storing at the mobile device a history of neighboring cells for the mobile device, the historical information comprising the history of neighboring cells.
 7. The method of claim 1, wherein the subset comprises a single neighboring cell, the method further comprising: measuring a signal strength associated with the single neighboring cell; and performing a handover or cell reselection of the mobile device to the single neighboring cell when the signal strength is greater than a threshold level.
 8. The method of claim 7, further comprising: determining not to perform measurements of neighboring cells other than the single neighboring cell when the signal strength associated with the single neighboring cell is greater than the threshold level.
 9. The method of claim 1, wherein the identifying the subset comprises: determining a quality metric for each of the neighboring cells in the set, the subset comprising the neighboring cells that have a quality metric greater than a threshold level.
 10. The method of claim 9, wherein the quality metric of each neighboring cell is based on at least a data rate associated with the neighboring cell, or an ability of the neighboring cell to perform offloading to an alternate radio access technology, or a projected amount of time for which the mobile device will remain connected to the neighboring cell.
 11. The method of claim 9, further comprising: ranking the neighboring cells in the subset according to their respective quality metrics.
 12. The method of claim 9, further comprising: determining a confidence level for each of the neighboring cells in the set, the quality metric of each cell being based on at least the confidence level of that cell, the subset comprising the neighboring cells that have a confidence level greater than a threshold level.
 13. The method of claim 12 further comprising: identifying one of the neighboring cells in the subset with a confidence level greater than a threshold level; and performing a blind handover of the mobile device to the one neighboring cell.
 14. The method of claim 1, further comprising: determining a frequency with which measurements are performed by the mobile device for the neighboring cells based on the historical information.
 15. The method of claim 1, further comprising: excluding at least one of the neighboring cells from the subset based at least on a current speed of the mobile device and a signal strength of the at least one of the neighboring cells.
 16. The method of claim 15, wherein the at least one of the neighboring cells is excluded based on a determination that performing a handover to the at least one of the neighboring cells will result in a ping pong effect.
 17. The method of claim 1, further comprising: adjusting at least one cell measurement associated with handover or reselection based on the historical information.
 18. The method of claim 1, further comprising: adjusting at least one handover or reselection criterion parameter based on the historical information.
 19. The method of claim 1, further comprising: selectively enabling or disabling an air interface of the mobile device based on the historical information and a current location of the mobile device.
 20. The method of claim 1, wherein the mobility patterns of the mobile device comprise a route and schedule between a first location and a second location.
 21. The method of claim 1, wherein the mobility patterns of the mobile device comprise a location and a period of time during which the mobile device remains at the location.
 22. An apparatus for managing wireless communications, comprising: a processor; and a memory in electronic communication with the processor, the memory embodying instructions, the instructions being executable by the processor to: identify a subset of a set of neighboring cells for measurement by a mobile device, the identification based on historical information associated with mobility patterns of the mobile device.
 23. The apparatus of claim 22, wherein the apparatus comprises at least one of: a network entity or the mobile device.
 24. The apparatus of claim 22, wherein the instructions are further executable by the processor to: receive the historical information from a server, the historical information comprising a serving cell history of the mobile device over a predetermined period of time collected by the server.
 25. The apparatus of claim 24, wherein the historical information further comprises a history of neighboring cells for the mobile device.
 26. The apparatus of claim 22, wherein the instructions are further executable by the processor to: collect and store at the apparatus a serving cell history of the mobile device over a predetermined period of time, the historical information comprising the serving cell history.
 27. The apparatus of claim 26, wherein the instructions are further executable by the processor to: collect and store at the apparatus a history of neighboring cells for the mobile device, the historical information comprising the history of neighboring cells.
 28. The apparatus of claim 22, wherein the subset comprises a single neighboring cell and the instructions are further executable by the processor to: measure a signal strength associated with the single neighboring cell; and perform a handover or cell reselection of the mobile device to the single neighboring cell when the signal strength is greater than a threshold level.
 29. The apparatus of claim 28, wherein the instructions are further executable by the processor to: determine not to perform measurements of neighboring cells other than the single neighboring cell when the signal strength associated with the single neighboring cell is greater than the threshold level.
 30. The apparatus of claim 22, wherein the wherein the instructions are further executable by the processor to identify the subset by: determining a quality metric for each of the neighboring cells in the set, the subset comprising the neighboring cells that have a quality metric greater than a threshold level.
 31. The apparatus of claim 30, wherein the quality metric of each neighboring cell is based on at least a data rate associated with the neighboring cell, or an ability of the neighboring cell to perform offloading to an alternate radio access technology, or a projected amount of time for which the mobile device will remain connected to the neighboring cell.
 32. The apparatus of claim 30, wherein the instructions are further executable by the processor to: rank the neighboring cells in the subset according to their respective quality metrics.
 33. The apparatus of claim 30, wherein the instructions are further executable by the processor to: determine a confidence level for each of the neighboring cells in the set, the quality metric of each cell being based on at least the confidence level of that cell, the subset comprising the neighboring cells that have a confidence level greater than a threshold level.
 34. The apparatus of claim 33 wherein the instructions are further executable by the processor to: identify one of the neighboring cells in the subset with a confidence level greater than a threshold level; and perform a blind handover of the mobile device to the one neighboring cell.
 35. The apparatus of claim 22, wherein the instructions are further executable by the processor to: determine a frequency with which measurements are performed by the mobile device for the neighboring cells based on the historical information.
 36. The apparatus of claim 22, wherein the instructions are further executable by the processor to: exclude at least one of the neighboring cells from the subset based at least on a current speed of the mobile device and a signal strength of the at least one of the neighboring cells.
 37. The apparatus of claim 36, wherein the at least one of the neighboring cells is excluded based on a determination that performing a handover to the at least one of the neighboring cells will result in a ping pong effect.
 38. The apparatus of claim 22, wherein the instructions are further executable by the processor to: adjust at least one cell measurement associated with handover or reselection based on the historical information.
 39. The apparatus of claim 22, wherein the instructions are further executable by the processor to: adjust at least one handover or reselection criterion parameter based on the historical information.
 40. The apparatus of claim 22, wherein the instructions are further executable by the processor to: selectively enable or disable an air interface of the mobile device based on the historical information and a current location of the mobile device.
 41. The apparatus of claim 22, wherein the mobility patterns of the mobile device comprise a route and schedule between a first location and a second location.
 42. The apparatus of claim 22, wherein the mobility patterns of the mobile device comprise a location and a period of time during which the mobile device remains at the location.
 43. An apparatus for managing wireless communications, comprising: means for identifying a subset of a set of neighboring cells for measurement by a mobile device, the identification based on historical information associated with mobility patterns of the mobile device.
 44. The apparatus of claim 43, wherein the apparatus comprises at least one of: a network entity or the mobile device.
 45. The apparatus of claim 43, further comprising: means for receiving the historical information from a server, the historical information comprising a serving cell history of the mobile device over a predetermined period of time collected by the server.
 46. The apparatus of claim 43, wherein the historical information further comprises a history of neighboring cells for the mobile device.
 47. The apparatus of claim 43, further comprising: means for collecting and storing at the mobile device a serving cell history of the mobile device over a predetermined period of time, the historical information comprising the serving cell history.
 48. The apparatus of claim 43, further comprising: means for collecting and storing at the mobile device a history of neighboring cells for the mobile device, the historical information comprising the history of neighboring cells.
 49. The apparatus of claim 43, wherein the subset comprises a single neighboring cell, the apparatus further comprising: means for measuring a signal strength associated with the single neighboring cell; and means for performing a handover or cell reselection of the mobile device to the single neighboring cell when the signal strength is greater than a threshold level.
 50. The apparatus of claim 49, further comprising: means for determining not to perform measurements of neighboring cells other than the single neighboring cell when the signal strength associated with the single neighboring cell is greater than the threshold level.
 51. The apparatus of claim 43, wherein the means for identifying the subset comprises: means for determining a quality metric for each of the neighboring cells in the set, the subset comprising the neighboring cells that have a quality metric greater than a threshold level.
 52. The apparatus of claim 51, wherein the quality metric of each neighboring cell is based on at least a data rate associated with the neighboring cell, or an ability of the neighboring cell to perform offloading to an alternate radio access technology, or a projected amount of time for which the mobile device will remain connected to the neighboring cell.
 53. The apparatus of claim 51, further comprising: means for ranking the neighboring cells in the subset according to their respective quality metrics.
 54. The apparatus of claim 51, further comprising: means for determining a confidence level for each of the neighboring cells in the set, the quality metric of each cell being based on at least the confidence level of that cell, the subset comprising the neighboring cells that have a confidence level greater than a threshold level.
 55. The apparatus of claim 54 further comprising: means for identifying one of the neighboring cells in the subset with a confidence level greater than a threshold level; and means for performing a blind handover of the mobile device to the one neighboring cell.
 56. The apparatus of claim 43, further comprising: means for determining a frequency with which measurements are performed by the mobile device for the neighboring cells based on the historical information.
 57. The apparatus of claim 43, further comprising: means for excluding at least one of the neighboring cells from the subset based at least on a current speed of the mobile device and a signal strength of the at least one of the neighboring cells.
 58. The apparatus of claim 57, wherein the at least one of the neighboring cells is excluded based on a determination that performing a handover to the at least one of the neighboring cells will result in a ping pong effect.
 59. The apparatus of claim 43, further comprising: means for adjusting at least one cell measurement associated with handover or reselection based on the historical information.
 60. The apparatus of claim 43, further comprising: means for adjusting at least one handover or reselection criterion parameter based on the historical information.
 61. The apparatus of claim 43, further comprising: means for selectively enabling or disabling an air interface of the mobile device based on the historical information and a current location of the mobile device.
 62. The apparatus of claim 43, wherein the mobility patterns of the mobile device comprise a route and schedule between a first location and a second location.
 63. The apparatus of claim 43, wherein the mobility patterns of the mobile device comprise a location and a period of time during which the mobile device remains at the location.
 64. A computer program product for managing wireless communications, the computer program product comprising a non-transitory computer-readable storage medium comprising instructions executable by a processor to: identify a subset of a set of neighboring cells for measurement by a mobile device, the identification based on historical information associated with mobility patterns of the mobile device. 